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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 | 1x 1x 1x 1x 1x 1x 13x 13x 13x 13x 13x 5x 30008x 30013x 1x 30012x 30000x 30012x 1x 30011x 1x 30010x 30010x 180060x 912969x 180061x 912969x 912969x 589383x 912969x 2x 11655x 11655x 11655x 11655x 1144x 11655x 2x 2x 2x 2x 2x 1x 91x 635x 91x 2x 2x 2x 2x 2x 1x 1x 1x 480x 1x 1x 1x 566x 663x 566x 1x 5477598x 5x 5x 30x 70002x 5x 11659x 11659x 1x 91x 83091x 1053x 1053x 1053x 1x 23314x 23314x 23313x 1723507x 861754x 861753x | const SimpleCluster = require('./lib/simple-cluster') const util = require('./lib/util') const moment = require('moment') const natural = require('natural') const NGrams = natural.NGrams module.exports = class Ramekin { constructor (options) { this.options = { // a threshold for the minimum number of times a phrase has to occur // in a single day before it can even be considered a trend for a given subject. // @todo: work out a logical way of calculating this per category. minTrendFreq: 3, // the context of the number of days to consider for the history historyDays: 90, // the maximum size of the n-gram window maxN: 6, // remove stop words - why wouldn't you?! keepStops: false, // really not sure why I added this...assume it is to handle words that just didn't get mentioned in the history period. historyFrequencyTolerance: 1.6, // @todo: This is no longer used...(but I really think it should be) similarityThreshold: 0.3, // the maximum number of results to return. trendsTopN: 8, ...options} // initialise the multi-dimensional ngram array storage this.ngrams = new Array(this.options.maxN + 1).fill([]) // track the usage of the ngrams this.ngramHistory = {} // the documents this.docs = {} // setup stemming natural.PorterStemmer.attach() } /** * ingestAll() ingests a set of documents into the current Ramekin. * @param {docs} a set of documents in the format expected format */ ingestAll (docs) { docs.forEach(doc => { this.ingest(doc) }) } /** * Ingest a single document into the ramekin. * * @param doc document to ingest, in this format: * { * _id: <Unique ID - can be any format>, * body: "Text", * date: <ISO Date format string, or JavaScript date object>, * subject: <Any object> * } */ ingest (doc) { // preprocess the date to check it's in the right format. if (!doc.date) { throw new Error('No \'date\' field set for document - bit hard to do a time/series anaylsis without a date!') } if (!(doc.date instanceof Date)) { doc.date = new Date(doc.date) } // ensure there is an id set if (!doc.hasOwnProperty('_id')) { throw new Error('No \'_id\' field set for document') } // throw error if the document already exists in the ramekin if (this.docs.hasOwnProperty(doc._id)) { throw new Error(`Document ${doc._id} has already been added to the ramekin`) } // we may need to revisit what doc data we store this.docs[doc._id] = doc // generate all the [1...n]-grams for the document for (let n = 1; n <= this.options.maxN; n++) { // create ngrams from the normalised text let ngrams = NGrams.ngrams(this.normalise(doc.body), n) // ingest all the ngrams ngrams.forEach(ngram => { this.ingestNGram(ngram, doc) }) } } /** * Text analysis stage to take some raw text and convert * it into a format that we can ingest optimally. * @todo: create a function to map the original text * with the normalised version. */ normalise (s) { // normalise the body text (handling stop words) return s.tokenizeAndStem(this.options.keepStops).join(' ') } /** * Add a new ngram into the ramekin. */ ingestNGram (ngram, doc) { // construct the storable ngram object this.ngrams[ngram.length].push({ date: doc.date, ngram, subject: doc.subject }) // hash the historical data if (!this.ngramHistory.hasOwnProperty(ngram)) { this.ngramHistory[ngram] = { occurances: [] } } this.ngramHistory[ngram].occurances.push({date: doc.date, doc_id: doc._id}) } trendUsedPhrases (usedPhrases, { start, end, historyStart, historyEnd }) { // score each phrase from the trend period compared to it's historic use return usedPhrases.reduce((acc, phrase) => { // score if the phrase has trended in the last 24 hours const trendDocs = this.findDocs(phrase, { start, end }) const historyRangeCount = this.count(phrase, { start: historyStart, end: historyEnd }) const historyDayAverage = (historyRangeCount / this.options.historyDays) * this.options.historyFrequencyTolerance // if it's above the average if ((trendDocs.length > this.options.minTrendFreq) && (trendDocs.length > historyDayAverage)) { acc.push({ phrase, score: (trendDocs.length / (historyDayAverage + 1)) * phrase.length, historyRangeCount, trendRangeCount: trendDocs.length, docs: trendDocs }) } return acc }, []) } buildSearchCriteria(initialOptions = {}) { const start = initialOptions.start || moment().subtract(1, 'day').toDate() const end = initialOptions.end || new Date() const historyEnd = initialOptions.historyEnd || initialOptions.start || moment().subtract(1, 'day').toDate() const historyStart = initialOptions.historyStart || moment(historyEnd).subtract(this.options.historyDays, 'day').toDate() return { start, end, historyEnd, historyStart } } static getDocPhrasesFromTrends (trendPhrases) { return trendPhrases.reduce((acc, {docs, phrase}) => { docs.forEach(doc => { acc[doc] = (acc[doc] || []).concat([ phrase ]) }) return acc }, {}) } /** * Validate the trending options, setting defaults where necessary. * @todo: this whole block is manky and needs a refactor - setup, search and cluster */ trending (initialOptions = {}) { const searchOptions = this.buildSearchCriteria(initialOptions) // start of trending:search // find all the common phrases used in respective subject, over the past day const usedPhrases = this.usedPhrases(searchOptions) console.log(`There are ${usedPhrases.length} used phrases and ${Object.keys(this.docs).length} docs`) // duplicated data used later for sorting let trendPhrases = this.trendUsedPhrases(usedPhrases, searchOptions) if (trendPhrases.length === 0) return [] // remove sub phrases (i.e. "Tour de", compared to "Tour de France") trendPhrases = this.removeSubPhrases(trendPhrases) const docPhrases = this.constructor.getDocPhrasesFromTrends(trendPhrases) // rank results - @todo: needs making nicer trendPhrases.sort((a, b) => b.score === a.score ? b.phrase.length - a.phrase.length : b.score - a.score ) // cluster similar trends - find the phrase that is most similar to so many // others (i.e. i, where sum(i) = max( sum() ) const sc = new SimpleCluster(trendPhrases) const trends = sc.cluster() // rank the documents in each cluster, based on the docs etc. trends.forEach(trend => { const docs = trend.docs.map(doc => ({ doc, matches: util.intersection(docPhrases[doc], trend.phrases).length })) docs.sort((a, b) => b.matches - a.matches) // remove unnecessary sort data now it is sorted trend.docs = docs.map(doc => doc.doc) }) return trends }// currently line 280 /** * Finds the phrases used in a particular date range. * @todo: error handling. * @todo: this may be the main bottle neck - if a hashmap is created, * it reduces the searches and just sets the value each time. * returning just the values (or keys) would be quick?? */ usedPhrases ({start, end}) { const filterRow = row => row.date >= start && row.date < end const phrases = new Set() // load all the unique phrases for (let n = 1; n <= this.options.maxN; n++) { this.ngrams[n].filter(filterRow).forEach(row => { phrases.add(row.ngram) }) } return [...phrases] }// currently line 307 /** * Count the number of times that an ngrams has occurred within the * conditions of the options. * * @param ngram * @param options * @return int */ count (ngram, options) { let matchingDocs = this.findDocs(ngram, options) return matchingDocs.length } /** * Preprocess the results to only retain the longest phrases. For example, * if we have "Tour de France", we don't really remove noise. Fo * Improvement: potentially sort results by length before processing. * @todo: move to trending component. */ removeSubPhrases (trendPhrases) { for (let i = 0; i < trendPhrases.length; i++) { for (let j = i + 1; j < trendPhrases.length; j++) { if (util.isSubPhrase(trendPhrases[i].phrase, trendPhrases[j].phrase)) { // keep the biggest one const spliceI = trendPhrases[i].length > trendPhrases[j].length ? j : i // remove the element from the array trendPhrases.splice(spliceI, 1) // start processing again from the element that was cut out i = j = spliceI } } } return trendPhrases } /** * Find all the doc ids for a given ngram, matching the options. */ findDocs (ngram, options) { const history = this.ngramHistory[ ngram ] if (history === undefined) return [] return history.occurances.reduce((acc, doc) => { if ((doc.date >= options.start && doc.date < options.end) && (!options.hasOwnProperty('subject') || options.subject === this.docs[ doc.doc_id ].subject)) { return acc.concat(doc.doc_id) } return acc }, []) } } |